<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Spark on Data Simplicity</title><link>https://nobledynamic.github.io/tags/spark/</link><description>Recent content in Spark on Data Simplicity</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 Noble Dynamic Limited</copyright><lastBuildDate>Mon, 08 Apr 2024 09:37:43 +0000</lastBuildDate><atom:link href="https://nobledynamic.github.io/tags/spark/index.xml" rel="self" type="application/rss+xml"/><item><title>Fabric Madness: Feature Engineering with pyspark</title><link>https://nobledynamic.github.io/posts/fabric-madness-2/</link><pubDate>Mon, 08 Apr 2024 09:37:43 +0000</pubDate><guid>https://nobledynamic.github.io/posts/fabric-madness-2/</guid><description>In part 2 of this series we dive deeper into the process of feature engineering, a crucial part of the development lifecycle for any Machine Learning (ML) systems.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://nobledynamic.github.io/posts/fabric-madness-2/feature.webp"/></item></channel></rss>